Monday, May 21, 2012

Data Is More Important Than Algorithms

In 2006 Netflix offered to pay a million dollar, popularly known as the Netflix Prize, to whoever could help Netflix improve their recommendation system by at least 10%. A year later Korbel team won the Progress Prize by improving Netflix's recommendation system by 8.43%. They also gave the source code to Netflix of their 107 algorithms and 2000 hours of work. Netflix looked at these algorithms and decided to implement two main algorithms out of it to improve their recommendation system. Netflix did face some challenges but they managed to deploy these algorithms into their production system.

"We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment. Also, our focus on improving Netflix personalization had shifted to the next level by then."

This appears to be strange on the surface but when you examine the details it totally makes sense.

The cost to implement algorithms to achieve incremental improvement isn't simply justifiable. While the researchers worked hard on innovating the algorithms Netflix's business as well as their customers' behavior changed. Netflix saw more and more devices being used by their users to stream movies as opposed to get a DVD in mail. The main intent behind the million dollar prize for Netflix was to perfect their recommendation system for their DVD subscription plan since those subscribers carefully picked the DVDs recommended to them as it would take some time to receive those titles in mail. Customers wanted to make sure that they don't end up with lousy movies. Netflix didn't get any feedback regarding those titles until after their customers had viewed them and decided to share their ratings.

This customer behavior changed drastically when customers started following recommendations in realtime for their streaming subscription. They could instantaneously try out the recommended movies and if they didn't like them they tried something else. The barrier to get to the next movie that the customers might like significantly went down. Netflix also started to receive feedback in realtime while customers watched the movies. This was a big shift in user behavior and hence in recommendation system as customers moved from DVD to streaming.

What does this mean to the companies venturing into Big Data?

Algorithms are certainly important but they only provide incremental value on your existing business model. They are very difficult to innovate and way more expensive to implement. Netflix had a million dollar prize to attract the best talent, your organization probably doesn't. Your organization is also less likely to open up your private data into the public domain to discover new algorithms. I do encourage to be absolutely data-driven and do everything that you can to have data as your corporate strategy including hiring a data a scientist. But, most importantly, you should focus on your changing business — disruption and rapidly changing customer behavior — and data and not on algorithms. One of the promises of Big Data is to leave no data source behind. Your data is your business and your business is your data. Don't lose sight of it. Invest in technology and more importantly in people who have skills to stay on top of changing business models and unearth insights from data to strengthen and grow business. Algorithms are cool but the data is much cooler.

About me

Chirag MehtaVice President, Product Management and Business Development at SAP.

Email: tochirag at gmail dot com

• A Silicon Valley software executive with entrepreneurial, outside-in, and “General Manager” mindset with 15+ years of experience in product strategy, product design, product architecture, product management, and product development in building and shipping multiple releases of world-class enterprise software—applications, platform, middleware and infrastructure components—for the #1 and #2 enterprise software companies in the world.

• A keynote speaker and a panelist at several enterprise software conferences

• Adjunct faculty with the department of Computer Engineering at Santa Clara University teaching graduate classes

In my current role at SAP, I'm responsible for:

a) Product management activities to explore, identify, and realize Big Data breakthrough scenarios—things that were not feasible before or things customers could not even have imagined they could do—for SAP's most strategic customers, globally, across all industries.

b) Business Development activities to generate net new opportunities as well as developing existing opportunities that contribute to a very large percentage of overall revenue of SAP HANA, the fastest growing product in SAP's history.

c) Working closely with SAP's leadership as well as the executive management team to help them understand latent needs of SAP's customers and drive huge impact by involving them into the customer co-innovation process.

d) Leading and managing a highly skilled global diverse team of product managers and business development managers.

This is my personal blog and all views expressed are solely mine and not of my current or past employers'. You can also read my other non-technical personal blog: Cutting Chai

If you are reaching out to me for a speaking engagement, please include as much details as you can in your email to expedite my response. I don't take any product briefings and I don't "cover" products or companies.

What's on my Kindle?

Thinking, Fast and Slow: Daniel Kahneman, recipient of the Nobel Prize in Economic Sciences for his seminal work in psychology that challenged the rational model of judgment and decision making, is one of our most important thinkers. His ideas have had a profound and widely regarded impact on many fields—including economics, medicine, and politics—but until now, he has never brought together his many years of research and thinking in one book.

In the highly anticipated Thinking, Fast and Slow, Kahneman takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. Kahneman exposes the extraordinary capabilities—and also the faults and biases—of fast thinking, and reveals the pervasive influence of intuitive impressions on our thoughts and behavior. The impact of loss aversion and overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the challenges of properly framing risks at work and at home, the profound effect of cognitive biases on everything from playing the stock market to planning the next vacation—each of these can be understood only by knowing how the two systems work together to shape our judgments and decisions.

Engaging the reader in a lively conversation about how we think, Kahneman reveals where we can and cannot trust our intuitions and how we can tap into the benefits of slow thinking. He offers practical and enlightening insights into how choices are made in both our business and our personal lives—and how we can use different techniques to guard against the mental glitches that often get us into trouble. Thinking, Fast and Slow will transform the way you think about thinking.

Boomerang: Travels in the New Third World
The tsunami of cheap credit that rolled across the planet between 2002 and 2008 was more than a simple financial phenomenon: it was temptation, offering entire societies the chance to reveal aspects of their characters they could not normally afford to indulge.

Icelanders wanted to stop fishing and become investment bankers. The Greeks wanted to turn their country into a piñata stuffed with cash and allow as many citizens as possible to take a whack at it. The Germans wanted to be even more German; the Irish wanted to stop being Irish.

Michael Lewis's investigation of bubbles beyond our shores is so brilliantly, sadly hilarious that it leads the American reader to a comfortable complacency: oh, those foolish foreigners. But when he turns a merciless eye on California and Washington, DC, we see that the narrative is a trap baited with humor, and we understand the reckoning that awaits the greatest and greediest of debtor nations.

Disclaimer: All the views expressed in this Blog are solely mine and not of my current or past employers'.